更新时间：2021-06-18 18:29:09
封面
版权信息
About Packt
Why subscribe?
About the authors
Preface
Section 1: Introduction
Understanding the Basics of NLP
Programming languages versus natural languages
Why should I learn NLP?
Current applications of NLP
Summary
NLP Using Python
Technical requirements
Understanding Python with NLP
Important Python libraries
Web scraping libraries and methodology
Overview of Jupyter Notebook
Section 2: Natural Language Representation and Mathematics
Building Your NLP Vocabulary
Lexicons
Phonemes graphemes and morphemes
Tokenization
Understanding word normalization
Transforming Text into Data Structures
Understanding vectors and matrices
Exploring the Bag-of-Words architecture
TF-IDF vectors
Distance/similarity calculation between document vectors
One-hot vectorization
Building a basic chatbot
Word Embeddings and Distance Measurements for Text
Understanding word embeddings
Demystifying Word2vec
Training a Word2vec model
Word mover’s distance
Exploring Sentence- Document- and Character-Level Embeddings
Venturing into Doc2Vec
Exploring fastText
Understanding Sent2Vec and the Universal Sentence Encoder
Section 3: NLP and Learning
Identifying Patterns in Text Using Machine Learning
Introduction to ML
Data preprocessing
The Naive Bayes algorithm
The SVM algorithm
Productionizing a trained sentiment analyzer
From Human Neurons to Artificial Neurons for Understanding Text
Exploring the biology behind neural networks
How does a neural network learn?
Understanding regularization
Let's talk Keras
Building a question classifier using neural networks
Applying Convolutions to Text
What is a CNN?
Detecting sarcasm in text using CNNs
Capturing Temporal Relationships in Text
Baby steps toward understanding RNNs
Vanishing and exploding gradients
Architectural forms of RNNs
Giving memory to our networks – LSTMs
Building a text generator using LSTMs
Exploring memory-based variants of the RNN architecture
State of the Art in NLP
Seq2Seq modeling
Translating between languages using Seq2Seq modeling
Let's pay some attention
Transformers
BERT
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